Global Setting
Installing and Loading the R package
maptools is for creating spatial* objects
sf is for importing geospatial data
raster is for creating raster object
spatstat is for performing SPPA
tmap is for plotting thematic maps
tidyverse is for data handling
packages = c('maptools', 'sf', 'raster','spatstat', 'tmap','tidyverse','plotly','ggthemes' )
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}
Importing the Geospatial Data
Importing shapefile using st_read() of sf package. The output object is in tittle sf object class.
mpsz_sf <- st_read(dsn = "data/shapefile",
layer = "MP14_SUBZONE_WEB_PL")
Reading layer `MP14_SUBZONE_WEB_PL' from data source
`C:\nxinyan\IS415\IS415_blog-1\_posts\2021-10-26-in-class-exercise-5\data\shapefile'
using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21Projection is in SVY21
Importing aspatial data from rds folder
read_rds() of readr package is used instead of readRDS() of base R is used. This is because output of read_rds() is in tibble object
childcare <- read_rds("data/rds/childcare.rds")
CHAS <- read_rds("data/rds/CHAS.rds")
Note that there are some data issue in childcare data frame because Lat and Lng should be in numeric data type. The coordinate fields are in decimal degrees. Hence, wgs referencing system is assumed.
Converting the aspatial data frame into sf objects
CHAS_sf <- st_as_sf(CHAS,
coords = c("X_COORDINATE",
"Y_COORDINATE"),
crs=3414)
Note: st_as_sf accept coordinates in character data type.
childcare_sf <- st_as_sf(childcare,
coords = c("Lng",
"Lat"),
crs=4326) %>%
st_transform(crs = 3414)
Plotting for reviewing childcare and CHAS
tmap_mode('view')
tm_shape(childcare_sf) +
tm_dots(alpha = 0.4,
col = "blue",
size = 0.05) +
tm_shape(CHAS_sf) +
tm_dots(alpha = 0.4,
col = "red",
size = 0.05)
Geospatial Data Wrangling
Converting frpm sf to Spatial* Classes
as_Spatial() of sf package.
childcare <- as_Spatial(childcare_sf)
CHAS <- as_Spatial(CHAS_sf)
mpsz <- as_Spatial(mpsz_sf)
Convert Spatial* dataframe into Spatial* objects
as.SpatialPoint() of as.SpatialPolygon() of maptools package
childcare_sp <- as(childcare, "SpatialPoints")
CHAS_sp <- as(CHAS, "SpatialPoints")
mpsz_sp <- as(mpsz, "SpatialPolygons")
Converting from Spatial* objecys into ppp objects
Using as.ppp() of maptools package,
childcare_ppp <- as(childcare_sp, "ppp")
CHAS_ppp <- as(CHAS_sp, "ppp")
Removing duplicate points using jitter
childcare_ppp_jit <- rjitter(childcare_ppp,
retry = TRUE,
nsim = 1,
drop = TRUE)
any(duplicated(childcare_ppp_jit))
[1] FALSECHAS_ppp_jit <- rjitter(CHAS_ppp,
retry = TRUE,
nsim = 1,
drop = TRUE)
any(duplicated(CHAS_ppp_jit))
[1] FALSEExtracting Punggol Planning Area
pg <- mpsz[mpsz@data$PLN_AREA_N=="PUNGGOL",]
Converting SpatialPolygonsDataFrame into SpatialPolygons object
pg_sp <- as(pg, "SpatialPolygons")
Converting SpatialPolygons into owin object
pg_owin <- as(pg_sp, "owin")
Extracting spatial points within owin
childcare_pg <- childcare_ppp_jit[pg_owin]
CHAS_pg <- CHAS_ppp_jit[pg_owin]
plot(childcare_pg)
L-function
L_childcare <- envelope(childcare_pg,
Lest,
nsim=99,
rank=1,
global = TRUE)
Generating 99 simulations of CSR ...
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99.
Done.